This page describes CAIDA's participation in the collaborative project
Named Data Networking (NDN) supported by NSF grant CNS-1039646. This project
is one of the four Future Internet Architecture Awards.

We found the hyperbolic coordinates of the nodes participating in the NDN
testbed, simulated the greedy forwarding between these nodes, and measured
the success ratio and stretch for the full graph and for the graphs obtained
from it by all possible one-link removals that do not disconnect the full graph.

NDN Project Overview

We are exploring a new Internet architecture called "Named Data
Networking" (NDN). By naming data instead of locations, this
architecture will transition the Internet from its focus today on
"where" -- addresses and hosts -- to "what" -- the content that users
and applications care about.

The NDN team consists of a diverse mix of over 20 researchers from
10 campuses bringing a wide spectrum of expertise to tackle the
ambitious interdisciplinary reserarch agenda and test the emerging
architecture against application needs. The researchers will investigate the
core set of problems
necessary to validate NDN as a future Internet architecture:

fundamental communication theory for the new case of storage as an
explicit part of the communication model.

This effort will provide a key missing link connecting several
architectural components developed under NSF's FIND program and
elsewhere, as well as identify new methods to resolve persistent
problems in the current Internet architecture.
Prototype implementations of both infrastructure and applications,
will be released as open source software so that results may
be broadly shared for research and education.

CAIDA's role in the NDN

The NDN team members determined the following six key areas of research:
Applications, Security, Routing and Forwarding, Evaluation and
Measurement, Theory, and Software Coordination.

Co-PI Claffy is the leader of Evaluation and Measurement team.

To evaluate the NDN architecture, researchers will use quantitative
and qualitative metrics that characterize its ability to support existing
and new applications, as well as the performance, scalability, and
robustness of NDN networks.

Evaluation methods will include measurements of NDN-connected testbeds,
user surveys, simulations, and theoretical analysis to identify
problems in the design and evaluate performance at different scales. The key metrics are:

total amount of information transported by the network in space and time (i.e. consumed entropy rate), traffic patterns compared with IP

testbed measurement, theoretical analysis

For simulations, we will use the best available data on Internet
structure, behavior, and usage patterns: traffic data from the DHS
PREDICT project, AS-level topology derived from Route Views data, and annotated router-level topology data from CAIDA.

Co-PI Krioukov is a member of two teams, Theory and Routing/Forwarding.

The NDN architecture is sufficiently flexible to allow us to explore
the most ambitious routing research idea to emerge from NSF's FIND
program: greedy routing based on underlying metric spaces.

Assuming routers are assigned coordinates in a name-based metric
space, they can compute the name-space distances between their directly
connected neighbors and the destination name in the Interest packet. The key to the scalability of this approach is the hierarchical, or
tree-like, name space structure. We have shown that even an approximately tree-like
structure can be mapped to an underlying hyperbolic metric space
which supports theoretically optimal routing performance characteristics when greedy routing is used.

The researchers will provide mathematical models of the name-space
metric structure, develop methods to assign name-space addresses to
routers, and design NDN routing algorithms using greedy routing and
random walks. They will also investigate what combinations of the
name-space structure and network topology ensure the high efficiency
of these algorithms. Our first experiments conducted on the NDN testbed demonstrated the success and robustness of greedy forwarding when using the underlying hyperbolic metric space to calculate the distance
between participating nodes.

Open research questions include:

how routers can compute the name-space coordinates using only local
information in their FIBs, and potentially the name-space coordinates
of their neighbors?

how routers can optimize their performance with probabilistic random
walk algorithm?

what are the specific properties that the structure of the name space
and router topology must have to ensure efficiency?